EntityDeletr/EAGLE3-gpt-oss-20b-GGUF

Quantized version of nebius/EAGLE3-gpt-oss-20b.

Their model card is pasted as is below.

Files:

• model.safetensors - original unquantized safetensors
• model.gguf - unquantized bf16 GGUF
• EAGLE3-gpt-oss-20b.gguf - GGUF quantized to Q5_K_M

---

Model Description

This is an EAGLE-3 draft-model for gpt-oss-20b, trained from scratch using LK losses — training objectives that directly target acceptance rate rather than using KL divergence as a proxy.

Training Details

• Base model: openai/gpt-oss-20b
• Draft architecture: EAGLE-3
• Training data: Infinity-Instruct-0625 with gpt-oss-20b generated responses
• Training objective: Hybrid LK loss with adaptive λ scheduling (η=3)
• Training: 10 epochs from random initialization
• Draft length: K = 6 speculative tokens

Performance

Average acceptance length (τ) measured across MT-bench, HumanEval, and GSM8K with K = 7:

Configuration	Temperature = 0	Temperature = 1
EAGLE-3 + KL	3.46	3.17
EAGLE-3 + LK (ours)	3.49	3.29

Comparison with Public Checkpoints

Model	MT-bench (τ)	HumanEval (τ)	GSM8K (τ)
RedHatAI/gpt-oss-20b-speculator.eagle3	2.63	2.43	3.00
Ours	3.20	3.01	3.65

Measured at temperature = 1 with K = 7

Note: Earlier vLLM versions sampled draft tokens greedily regardless of temperature, which underestimated acceptance rates at temperature > 0. Stochastic draft sampling was introduced in v0.18.0, and from v0.21.0 it can be enabled via speculative_config using rejection_sample_method and draft_sample_method. The acceptance metrics reported above were measured under standard rejection sampling and are reproducible with the configuration below.

Usage with vLLM

from vllm import LLM, SamplingParams

llm = LLM(
    model="openai/gpt-oss-20b",
    speculative_config={
        "method": "eagle3",
        "model": "nebius/EAGLE3-gpt-oss-20b",
        "num_speculative_tokens": 6,
        "rejection_sample_method": "standard",
        "draft_sample_method": "gumbel",
    },
)

sampling_params = SamplingParams(temperature=0.7)
outputs = llm.generate(["Explain speculative decoding in simple terms."], sampling_params)

License

CC BY 4.0

Citation

@misc{samarin2026lklosses,
  title     = {LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding},
  author    = {Alexander Samarin and Sergei Krutikov and Anton Shevtsov and Sergei Skvortsov and Filipp Fisin and Alexander Golubev},
  year      = {2026},
  eprint    = {2602.23881},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url       = {https://arxiv.org/abs/2602.23881}
}